US FORTUNE TOP 100 COMPANIES 2022¶

MUHAMMAD ARIENAL HAQ - PORTFOLIO

DEFINE THE TASK¶

  1. Comparison of the top ten companies based on some metrics (revenue, revenue change, profits, profits change, assets, market value, and employees).
  2. The relationship between rank, performance, and value.

COLLECT THE DATA¶

Dataset Source:¶

https://www.kaggle.com/datasets/ramjasmaurya/fortune-1000-companieslatest¶
In [1]:
# Import necessary libraries
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.subplots as sp
In [2]:
# Import US fortune 1000 companies 2022 CSV file
fortune_df = pd.read_csv('E:\\Data Analyst Mastery\\Fortune Top 1000 Companies Analysis\\fortune 1000 companies in 2022 - 2022.csv')
fortune_df.head(10)
Out[2]:
rank in 2022 Name Revenue revenue(% change) profits in millions profits % change\r assets market value change in rank of top 1000 companies employees change in rank(500 only)\r\n
0 1 Walmart $572,754 2.40% $13,673 1.20% $244,860 $409,795 - 2,300,000 -
1 2 Amazon $469,822 21.70% $33,364 56.40% $420,549 $1,658,807.30 - 1,608,000 -
2 3 Apple $365,817 33.30% $94,680 64.90% $351,002 $2,849,537.60 - 154,000 -
3 4 CVS Health $292,111 8.70% $7,910 10.20% $232,999 $132,839.20 - 258,000 -
4 5 UnitedHealth Group $287,597 11.80% $17,285 12.20% $212,206 $479,830.30 - 350,000 -
5 6 Exxon Mobil $285,640 57.40% $23,040 - $338,923 $349,652.40 4 63,000 4
6 7 Berkshire Hathaway $276,094 12.50% $89,795 111.20% $958,784 $779,542.30 -1 372,000 -1
7 8 Alphabet $257,637 41.20% $76,033 88.80% $359,268 $1,842,326.10 1 156,500 1
8 9 McKesson $238,228 3.10% -$4,539 -604.30% $65,015 $45,857.80 -2 67,500 -2
9 10 AmerisourceBergen $213,988.80 12.70% $1,539.90 - $57,337.80 $32,355.70 -2 40,000 -2

CLEAN THE DATA¶

In [3]:
# Create new dataframe for top 100 companies
companies_df = fortune_df.iloc[:100,:]
In [4]:
# Check the information on the dataframe
companies_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 11 columns):
 #   Column                                Non-Null Count  Dtype 
---  ------                                --------------  ----- 
 0   rank in 2022                          100 non-null    object
 1   Name                                  100 non-null    object
 2   Revenue                               100 non-null    object
 3   revenue(% change)                     100 non-null    object
 4   profits in millions                   100 non-null    object
                     100 non-null    object
 6   assets                                100 non-null    object
 7   market value                          100 non-null    object
 8   change in rank of top 1000 companies  100 non-null    object
 9   employees                             100 non-null    object
 10  change in rank(500 only)
            100 non-null    object
dtypes: object(11)
memory usage: 8.7+ KB
In [5]:
# Delete unneeded columns, because there are 2 identical columns
del companies_df['change in rank(500 only)\r\n']
companies_df.head(10)
Out[5]:
rank in 2022 Name Revenue revenue(% change) profits in millions profits % change\r assets market value change in rank of top 1000 companies employees
0 1 Walmart $572,754 2.40% $13,673 1.20% $244,860 $409,795 - 2,300,000
1 2 Amazon $469,822 21.70% $33,364 56.40% $420,549 $1,658,807.30 - 1,608,000
2 3 Apple $365,817 33.30% $94,680 64.90% $351,002 $2,849,537.60 - 154,000
3 4 CVS Health $292,111 8.70% $7,910 10.20% $232,999 $132,839.20 - 258,000
4 5 UnitedHealth Group $287,597 11.80% $17,285 12.20% $212,206 $479,830.30 - 350,000
5 6 Exxon Mobil $285,640 57.40% $23,040 - $338,923 $349,652.40 4 63,000
6 7 Berkshire Hathaway $276,094 12.50% $89,795 111.20% $958,784 $779,542.30 -1 372,000
7 8 Alphabet $257,637 41.20% $76,033 88.80% $359,268 $1,842,326.10 1 156,500
8 9 McKesson $238,228 3.10% -$4,539 -604.30% $65,015 $45,857.80 -2 67,500
9 10 AmerisourceBergen $213,988.80 12.70% $1,539.90 - $57,337.80 $32,355.70 -2 40,000
In [6]:
# Rename each header column
companies_df = companies_df.rename({'rank in 2022':'Rank','Revenue':'Revenue (Million USD)','revenue(% change)':'Revenue Change (%)','profits in millions':'Profits (Million USD)','profits % change\r':'Profits Change (%)','assets':'Assets (Million USD)','market value':'Market Value (Million USD)','change in rank of top 1000 companies':'Rank Change','employees':'Employees'}, axis=1)
companies_df.head(10)
Out[6]:
Rank Name Revenue (Million USD) Revenue Change (%) Profits (Million USD) Profits Change (%) Assets (Million USD) Market Value (Million USD) Rank Change Employees
0 1 Walmart $572,754 2.40% $13,673 1.20% $244,860 $409,795 - 2,300,000
1 2 Amazon $469,822 21.70% $33,364 56.40% $420,549 $1,658,807.30 - 1,608,000
2 3 Apple $365,817 33.30% $94,680 64.90% $351,002 $2,849,537.60 - 154,000
3 4 CVS Health $292,111 8.70% $7,910 10.20% $232,999 $132,839.20 - 258,000
4 5 UnitedHealth Group $287,597 11.80% $17,285 12.20% $212,206 $479,830.30 - 350,000
5 6 Exxon Mobil $285,640 57.40% $23,040 - $338,923 $349,652.40 4 63,000
6 7 Berkshire Hathaway $276,094 12.50% $89,795 111.20% $958,784 $779,542.30 -1 372,000
7 8 Alphabet $257,637 41.20% $76,033 88.80% $359,268 $1,842,326.10 1 156,500
8 9 McKesson $238,228 3.10% -$4,539 -604.30% $65,015 $45,857.80 -2 67,500
9 10 AmerisourceBergen $213,988.80 12.70% $1,539.90 - $57,337.80 $32,355.70 -2 40,000
In [7]:
# Find the string '-' in every column, except the negative sign
mask = np.column_stack([companies_df[col].astype(str).str.contains('(?:\s|^)-(?:\s|$)') for col in companies_df])
companies_df.loc[mask.any(axis=1)]
Out[7]:
Rank Name Revenue (Million USD) Revenue Change (%) Profits (Million USD) Profits Change (%) Assets (Million USD) Market Value (Million USD) Rank Change Employees
0 1 Walmart $572,754 2.40% $13,673 1.20% $244,860 $409,795 - 2,300,000
1 2 Amazon $469,822 21.70% $33,364 56.40% $420,549 $1,658,807.30 - 1,608,000
2 3 Apple $365,817 33.30% $94,680 64.90% $351,002 $2,849,537.60 - 154,000
3 4 CVS Health $292,111 8.70% $7,910 10.20% $232,999 $132,839.20 - 258,000
4 5 UnitedHealth Group $287,597 11.80% $17,285 12.20% $212,206 $479,830.30 - 350,000
5 6 Exxon Mobil $285,640 57.40% $23,040 - $338,923 $349,652.40 4 63,000
9 10 AmerisourceBergen $213,988.80 12.70% $1,539.90 - $57,337.80 $32,355.70 -2 40,000
12 13 AT&T $168,864 -1.70% $20,081 - $551,622 $169,262.40 -2 202,600
14 15 Cardinal Health $162,467 6.20% $611 - $44,453 $15,709.40 -1 46,827
15 16 Chevron $162,465 71.60% $15,625 - $239,535 $317,120.10 11 42,595
18 19 Marathon Petroleum $141,032 58.50% $9,738 - $85,373 $47,758.10 13 17,700
21 22 Ford Motor $136,341 7.20% $17,937 - $257,035 $67,958.20 -1 183,000
28 29 Phillips 66 $114,852 75.40% $1,317 - $55,594 $41,561 19 14,000
29 30 Valero Energy $108,332 80.20% $930 - $57,888 $41,572.20 23 9,804
41 42 State Farm Insurance $82,224.70 4.20% $1,280.90 -65.70% $325,349.30 - -3 53,586
50 51 Prudential Financial $70,934 24.40% $7,724 - $937,582 $44,482.20 4 40,916
51 52 Albertsons $69,690.40 11.60% $850.20 82.30% $26,598 $16,063.70 - 300,000
52 53 Walt Disney $67,418 3.10% $1,995 - $203,609 $249,718.10 -3 171,000
53 54 Energy Transfer $67,417 73.10% $5,470 - $105,963 $34,496.90 27 12,558
57 58 Raytheon Technologies $64,388 13.80% $3,864 - $161,404 $147,640.90 -1 174,000
59 60 Boeing $62,286 7.10% -$4,202 - $138,552 $113,058.70 -6 142,000
60 61 Morgan Stanley $61,121 17.40% $15,034 36.70% $1,188,140 $155,685.60 - 74,814
61 62 HCA Healthcare $58,752 14% $6,956 85.30% $50,742 $75,691.90 - 244,000
66 67 AIG $52,057 19% $9,388 - $596,112 $50,608.20 5 36,600
71 72 New York Life Insurance $51,198.50 9.60% $277.10 - $379,984.60 - -5 14,344
75 76 Publix Super Markets $48,393.90 7.10% $4,412.20 11.10% $31,524.30 - -7 232,000
76 77 ConocoPhillips $48,349 151.10% $8,079 - $90,661 $129,605.10 79 9,900
77 78 Liberty Mutual Insurance Group $48,200 10.10% $3,068 304.70% $156,043 - -7 45,000
79 80 Nationwide $47,376 13% $1,617.20 - $278,412.20 - -4 24,134
81 82 Bristol-Myers Squibb $46,385 9.10% $6,994 - $109,314 $155,203.50 -7 32,200
87 88 Plains GP Holdings $42,078 80.70% $60 - $29,978 $2,242.90 39 4,100
89 90 TIAA $40,526.40 -2.60% $4,060.70 627.60% $698,190.80 - -11 15,065
92 93 Coca-Cola $38,655 17.10% $9,771 26.10% $94,354 $268,769.70 - 79,000
94 95 CHS $38,448 35.40% $554 31.10% $17,576.30 - 8 9,941
95 96 USAA $37,469.60 3.20% $3,300 -15.50% $210,676.50 - -9 37,335
96 97 Northwestern Mutual $36,751.20 8.80% $977.80 130.10% $334,653.80 - -7 7,585
99 100 Massachusetts Mutual Life Insurance $35,899.50 51.70% $319.30 - $383,292.40 - 23 10,052
In [8]:
# Replaces the string '-' in each column with 0, except the negative sign
companies_df['Revenue Change (%)'] = companies_df['Revenue Change (%)'].str.replace('(?:\s|^)-(?:\s|$)', '0', regex=True)
companies_df['Profits (Million USD)'] = companies_df['Profits (Million USD)'].str.replace('(?:\s|^)-(?:\s|$)', '0', regex=True)
companies_df['Profits Change (%)'] = companies_df['Profits Change (%)'].str.replace('(?:\s|^)-(?:\s|$)', '0', regex=True)
companies_df['Market Value (Million USD)'] = companies_df['Market Value (Million USD)'].str.replace('(?:\s|^)-(?:\s|$)', '0', regex=True)
companies_df['Rank Change'] = companies_df['Rank Change'].str.replace('(?:\s|^)-(?:\s|$)', '0', regex=True)
companies_df['Employees'] = companies_df['Employees'].str.replace('(?:\s|^)-(?:\s|$)', '0', regex=True)
companies_df.head(10)
Out[8]:
Rank Name Revenue (Million USD) Revenue Change (%) Profits (Million USD) Profits Change (%) Assets (Million USD) Market Value (Million USD) Rank Change Employees
0 1 Walmart $572,754 2.40% $13,673 1.20% $244,860 $409,795 0 2,300,000
1 2 Amazon $469,822 21.70% $33,364 56.40% $420,549 $1,658,807.30 0 1,608,000
2 3 Apple $365,817 33.30% $94,680 64.90% $351,002 $2,849,537.60 0 154,000
3 4 CVS Health $292,111 8.70% $7,910 10.20% $232,999 $132,839.20 0 258,000
4 5 UnitedHealth Group $287,597 11.80% $17,285 12.20% $212,206 $479,830.30 0 350,000
5 6 Exxon Mobil $285,640 57.40% $23,040 0 $338,923 $349,652.40 4 63,000
6 7 Berkshire Hathaway $276,094 12.50% $89,795 111.20% $958,784 $779,542.30 -1 372,000
7 8 Alphabet $257,637 41.20% $76,033 88.80% $359,268 $1,842,326.10 1 156,500
8 9 McKesson $238,228 3.10% -$4,539 -604.30% $65,015 $45,857.80 -2 67,500
9 10 AmerisourceBergen $213,988.80 12.70% $1,539.90 0 $57,337.80 $32,355.70 -2 40,000
In [9]:
# Remove the strings '$' and ',' on each column
companies_df['Revenue (Million USD)'] = companies_df['Revenue (Million USD)'].str.replace('[$,]', '', regex=True)
companies_df['Revenue Change (%)'] = companies_df['Revenue Change (%)'].str.replace('[%]', '', regex=True)
companies_df['Profits (Million USD)'] = companies_df['Profits (Million USD)'].str.replace('[$,]', '', regex=True)
companies_df['Profits Change (%)'] = companies_df['Profits Change (%)'].str.replace('[%]', '', regex=True)
companies_df['Assets (Million USD)'] = companies_df['Assets (Million USD)'].str.replace('[$,]', '', regex=True)
companies_df['Market Value (Million USD)'] = companies_df['Market Value (Million USD)'].str.replace('[$,]', '', regex=True)
companies_df['Employees'] = companies_df['Employees'].str.replace('[,]', '', regex=True)
companies_df.head(10)
Out[9]:
Rank Name Revenue (Million USD) Revenue Change (%) Profits (Million USD) Profits Change (%) Assets (Million USD) Market Value (Million USD) Rank Change Employees
0 1 Walmart 572754 2.40 13673 1.20 244860 409795 0 2300000
1 2 Amazon 469822 21.70 33364 56.40 420549 1658807.30 0 1608000
2 3 Apple 365817 33.30 94680 64.90 351002 2849537.60 0 154000
3 4 CVS Health 292111 8.70 7910 10.20 232999 132839.20 0 258000
4 5 UnitedHealth Group 287597 11.80 17285 12.20 212206 479830.30 0 350000
5 6 Exxon Mobil 285640 57.40 23040 0 338923 349652.40 4 63000
6 7 Berkshire Hathaway 276094 12.50 89795 111.20 958784 779542.30 -1 372000
7 8 Alphabet 257637 41.20 76033 88.80 359268 1842326.10 1 156500
8 9 McKesson 238228 3.10 -4539 -604.30 65015 45857.80 -2 67500
9 10 AmerisourceBergen 213988.80 12.70 1539.90 0 57337.80 32355.70 -2 40000
In [10]:
# Change the data type of each column as needed
convert = {'Rank' : int, 'Revenue (Million USD)' : float, 'Revenue Change (%)' : float, 'Profits (Million USD)' : float, 'Profits Change (%)' : float, 'Assets (Million USD)' : float, 'Market Value (Million USD)' : float, 'Rank Change' : int, 'Employees' : int}
companies_df = companies_df.astype(convert)
In [11]:
# Check the data type of each column
companies_df.dtypes
Out[11]:
Rank                            int32
Name                           object
Revenue (Million USD)         float64
Revenue Change (%)            float64
Profits (Million USD)         float64
Profits Change (%)            float64
Assets (Million USD)          float64
Market Value (Million USD)    float64
Rank Change                     int32
Employees                       int32
dtype: object
In [12]:
# Check for the presence of null from each column
companies_df.isna().sum()
Out[12]:
Rank                          0
Name                          0
Revenue (Million USD)         0
Revenue Change (%)            0
Profits (Million USD)         0
Profits Change (%)            0
Assets (Million USD)          0
Market Value (Million USD)    0
Rank Change                   0
Employees                     0
dtype: int64
In [13]:
# Export DataFrame to Excel and CSV file
output_file_path_excel = 'E:\\Data Analyst Mastery\\Fortune Top 1000 Companies Analysis\\usFortuneTop100Companies2022Cleaned.xlsx'
output_file_path_csv = 'E:\\Data Analyst Mastery\\Fortune Top 1000 Companies Analysis\\usFortuneTop100Companies2022Cleaned.csv'
companies_df.to_excel(output_file_path_excel, index=False)
companies_df.to_csv(output_file_path_csv, sep='$', index=False)

ANALYZE, VISUALIZE THE DATA AND GAIN INSIGHTS¶

In [14]:
# Visualize Top 10 Based On Revenue and All Companies Revenue Change
n = 10

fig1 = px.bar(
    companies_df.nlargest(n, columns = 'Revenue (Million USD)'),
    x = 'Name',
    y = 'Revenue (Million USD)',
    color = 'Name',
    color_discrete_sequence = px.colors.sequential.thermal
)

fig2 = px.violin(
    companies_df,
    y ='Revenue Change (%)',
    box = True,
    points = 'all'
)

fig1_traces = []
fig2_traces = []
for trace in range(len(fig1['data'])):
    fig1_traces.append(fig1['data'][trace])
for trace in range(len(fig2['data'])):
    fig2_traces.append(fig2['data'][trace])

this_fig = sp.make_subplots(
    rows = 1, 
    cols = 2, 
    subplot_titles = ['<b>Top 10 Based On Revenue (Million USD)</b>', '<b>All Companies Revenue Change (%)</b>']
)
this_fig.update_layout(
    height = 500, 
    width = 1000, 
    font_family = 'verdana', 
    showlegend = False, 
    barmode = 'stack', 
    plot_bgcolor = 'rgb(245,245,245)'
)
for traces in fig1_traces:
    this_fig.append_trace(traces, row = 1, col = 1)
for traces in fig2_traces:
    this_fig.append_trace(traces, row = 1, col = 2)
this_fig.update_xaxes(fixedrange = True, tickangle = -45)
this_fig.update_yaxes(fixedrange = True, ticksuffix = ' ')
this_fig.show()

Insights:¶

  1. Based on the chart of the Top 10 Companies Based On Revenue, Walmart takes first place with 572,754 million USD, followed by Amazon with 469,822 million USD in second place, and Apple with 365,817 million USD in third place.
  2. Meanwhile, based on the All Companies Revenue Change chart, the average for increased revenue is in the range of 4.5% to 21.9%, then the highest increased revenue is at 151.1% and the lowest is at -21.4%.
In [15]:
# Visualize Top 10 Based On Profits and All Companies Profits Change
fig1 = px.bar(
    companies_df.nlargest(n, columns = 'Profits (Million USD)'),
    x = 'Name',
    y = 'Profits (Million USD)',
    color = 'Name',
    color_discrete_sequence = px.colors.sequential.thermal
)
fig2 = px.violin(
    companies_df,
    y = 'Profits Change (%)',
    box = True,
    points = 'all'
)

fig1_traces = []
fig2_traces = []
for trace in range(len(fig1['data'])):
    fig1_traces.append(fig1['data'][trace])
for trace in range(len(fig2['data'])):
    fig2_traces.append(fig2['data'][trace])
    
this_fig = sp.make_subplots(
    rows = 1, 
    cols = 2, 
    subplot_titles = ['<b>Top 10 Based On Profits (Million USD)</b>', '<b>All Companies Profits Change (%)</b>']
)
this_fig.update_layout(
    height = 500, 
    width = 1000, 
    font_family='verdana', 
    showlegend=False, 
    barmode='stack', 
    plot_bgcolor='rgb(245,245,245)'
)
for traces in fig1_traces:
    this_fig.append_trace(traces, row = 1, col = 1)
for traces in fig2_traces:
    this_fig.append_trace(traces, row = 1, col = 2)
this_fig.update_xaxes(fixedrange = True, tickangle = -45)
this_fig.update_yaxes(fixedrange = True, ticksuffix = ' ')
this_fig.show()

Insights:¶

  1. Based on the chart of the Top 10 Companies Based On Profits, Apple takes first place with 94,68 million USD, followed by Berkshire Hathaway with 89,795 million USD in second place, and Alphabet with 76,033 million USD in third place.
  2. Meanwhile, based on the All Companies Profits Change chart, the average for increased profits is in the range of 0% to 84.95%, then the highest increased profits is at 3,528.6% and the lowest is at -604.3%.
In [16]:
# Visualize Top 10 Based On Assets
fig = px.bar(
    companies_df.nlargest(n, columns = 'Assets (Million USD)'),
    x = 'Name',
    y = 'Assets (Million USD)',
    barmode = 'relative',
    color = 'Name',
    color_discrete_sequence = px.colors.sequential.thermal,
    height = 500,
    width = 550,
    title = '<b>Top 10 Companies Based On Assets</b>'
)

fig.update_layout(
    font_family = 'verdana', 
    title_font_size = 20, 
    title_x = 0.5, 
    xaxis_title = None, 
    yaxis_title = None, 
    showlegend = False,
    plot_bgcolor = 'rgb(245,245,245)'
)
fig.update_xaxes(fixedrange = True, tickangle = -45)
fig.update_yaxes(fixedrange = True, ticksuffix = ' ')
fig.show()

Insights:¶

  1. Based on the chart of the Top 10 Companies Based On Assets, Fannie Mae takes first place with 4,229,166 million USD, followed by JPMorgan Chase with 3,743,567 million USD in second place, and Bank of America with 3,169,495 million USD in third place.
In [17]:
# Visualize Top 10 Based On Market Value
fig = px.bar(
    companies_df.nlargest(n, columns = 'Market Value (Million USD)'),
    x = 'Name',
    y = 'Market Value (Million USD)',
    barmode = 'relative',
    color = 'Name',
    color_discrete_sequence = px.colors.sequential.thermal,
    height = 500,
    width = 550,
    title = '<b>Top 10 Companies Based On Market Value</b>'
)

fig.update_layout(
    font_family = 'verdana', 
    title_font_size = 20, 
    title_x = 0.5, 
    xaxis_title = None, 
    yaxis_title = None, 
    showlegend = False,
    plot_bgcolor = 'rgb(245,245,245)')
fig.update_xaxes(fixedrange = True, tickangle = -45)
fig.update_yaxes(fixedrange = True, ticksuffix = ' ')
fig.show()

Insights:¶

  1. Based on the chart of the Top 10 Companies Based On Market Value, Apple takes first place with 2,849,538 million USD, followed by Microsoft with 2,311,359 million USD in second place, and Alphabet with 1,842,326 million USD in third place.
In [18]:
# Visualize Top 10 Based On Employees
fig = px.bar(
    companies_df.nlargest(n, columns = 'Employees'),
    x = 'Name',
    y = 'Employees',
    barmode = 'relative',
    color = 'Name',
    color_discrete_sequence = px.colors.sequential.thermal,
    height = 500,
    width = 550,
    title = '<b>Top 10 Companies Based On Employees</b>',
)

fig.update_layout(
    font_family = 'verdana', 
    title_font_size = 20, 
    title_x = 0.5, 
    xaxis_title = None, 
    yaxis_title = None, 
    showlegend = False,
    plot_bgcolor = 'rgb(245,245,245)')
fig.update_xaxes(fixedrange = True, tickangle = -45)
fig.update_yaxes(fixedrange = True, ticksuffix = ' ')
fig.show()

Insights:¶

  1. Based on the chart of the Top 10 Companies Based On Employees, Walmart takes first place with 2.3 million people, followed by Amazon with 1.608 million people in second place, and Home Depot with 490.6 thousand people in third place.
In [19]:
# Visualize Relationship Between Rank and Performance
fig1 = px.scatter(
        companies_df,
        x = 'Rank',
        y = 'Revenue (Million USD)',
        color = 'Revenue (Million USD)',
        hover_data = ['Name'],
        trendline = 'ols'
        )
fig2 = px.scatter(
        companies_df,
        x = 'Rank',
        y = 'Profits (Million USD)',
        color = 'Profits (Million USD)',
        hover_data = ['Name'],
        trendline = 'ols'
        )

fig1_traces = []
fig2_traces = []
for trace in range(len(fig1['data'])):
    fig1_traces.append(fig1['data'][trace])
for trace in range(len(fig2['data'])):
    fig2_traces.append(fig2['data'][trace])
    
this_fig = sp.make_subplots(
    rows = 1, 
    cols = 2, 
    subplot_titles = ['<b>Rank and Revenue</b>', '<b>Rank and Profits</b>']
)
this_fig.update_layout(
    height = 500, 
    width = 1000, 
    font_family = 'verdana', 
    showlegend = False, 
    title = '<b>Relationship Between Rank and Performance<b>', 
    title_font_size = 20, 
    title_x = 0.5, 
    coloraxis_autocolorscale = False, 
    coloraxis_colorscale = ['rgb(0, 63, 92)', 'rgb(88, 80, 141)', 'rgb(188, 80, 144)', 'rgb(255, 99, 97)', 'rgb(255, 166, 0)'], 
    plot_bgcolor = 'rgb(245,245,245)'
)
for traces in fig1_traces:
    this_fig.append_trace(traces, row = 1, col = 1)
for traces in fig2_traces:
    this_fig.append_trace(traces, row = 1, col = 2)
this_fig.update_xaxes(fixedrange = True, tickangle = -45)
this_fig.update_yaxes(fixedrange = True, ticksuffix = ' ', type = 'log')

this_fig['layout']['xaxis']['title'] = 'Rank'
this_fig['layout']['xaxis2']['title'] = 'Rank'
this_fig.show()

col1, col2 = 'Rank', 'Revenue (Million USD)'
corr = companies_df[col1].corr(companies_df[col2])
print('1. Correlation between Rank and Revenue: ', round(corr, 2))
col1, col2 = 'Rank', 'Profits (Million USD)'
corr = companies_df[col1].corr(companies_df[col2])
print('2. Correlation between Rank and Profits: ', round(corr, 2))

1. Correlation between Rank and Revenue:  -0.77
2. Correlation between Rank and Profits:  -0.44

Insights:¶

  1. Based on the graph of the relationship between rank and performance, it can be concluded that most of the companies with lower ranks show lower revenues and profits because profit is part of revenue. Still, it does not rule out that there are some companies with higher ranks that have low profits, but not for revenue.
  2. Rank and revenue have a correlation of -0.77, which indicates that there is a strong negative (downward-sloping) linear relationship, while rank and profits have a correlation of -0.44, which indicates that they have a moderate negative (downhill-sloping) relationship.
In [20]:
# Visualize Relationship Between Rank and Values
fig1 = px.scatter(
        companies_df,
        x = 'Rank',
        y = 'Assets (Million USD)',
        color = 'Assets (Million USD)',
        hover_data = ['Name'],
        trendline = 'ols',
        )
fig2 = px.scatter(
        companies_df,
        x = 'Rank',
        y = 'Market Value (Million USD)',
        color = 'Market Value (Million USD)',
        hover_data = ['Name'],
        trendline = 'ols',
        )

fig1_traces = []
fig2_traces = []
for trace in range(len(fig1['data'])):
    fig1_traces.append(fig1['data'][trace])
for trace in range(len(fig2['data'])):
    fig2_traces.append(fig2['data'][trace])
    
this_fig = sp.make_subplots(
    rows = 1, 
    cols = 2, 
    subplot_titles = ['<b>Rank and Assets</b>', '<b>Rank and Market Value</b>']
)
this_fig.update_layout(
    height = 500, 
    width = 1000, 
    font_family = 'verdana', 
    showlegend = False, 
    title = '<b>Relationship Between Rank and Values<b>', 
    title_font_size = 20, 
    title_x = 0.5, 
    coloraxis_autocolorscale = False, 
    coloraxis_colorscale = ['rgb(0, 63, 92)', 'rgb(88, 80, 141)', 'rgb(188, 80, 144)', 'rgb(255, 99, 97)', 'rgb(255, 166, 0)'], 
    plot_bgcolor = 'rgb(245,245,245)'
)
for traces in fig1_traces:
    this_fig.append_trace(traces, row = 1, col = 1)
for traces in fig2_traces:
    this_fig.append_trace(traces, row = 1, col = 2)
this_fig.update_xaxes(fixedrange = True, tickangle = -45)
this_fig.update_yaxes(fixedrange = True, ticksuffix = ' ', type='log')
this_fig['layout']['xaxis']['title']='Rank'
this_fig['layout']['xaxis2']['title']='Rank'
this_fig.show()

col1, col2 = 'Rank', 'Assets (Million USD)'
corr = companies_df[col1].corr(companies_df[col2])
print('1. Correlation between Rank and Revenue: ', round(corr, 2))
col1, col2 = 'Rank', 'Market Value (Million USD)'
corr = companies_df[col1].corr(companies_df[col2])
print('2. Correlation between Rank and Profits: ', round(corr, 2))

1. Correlation between Rank and Revenue:  -0.14
2. Correlation between Rank and Profits:  -0.38

Insights:¶

  1. Based on the graph of the relationship between rank and value, it can be concluded that rank does not really affect the size of assets and market value, so there are many variations in the data.
  2. Rank and assets have a correlation of -0.14, which indicates that there is a weak negative (downhill-sloping) linear relationship, also rank and profits have a correlation of -0.38, which indicates that there is a weak negative (downhill-sloping) linear relationship.